Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and pr...

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Bibliographic Details
Main Authors: Hejblum, Boris P. (Author), Weber, Griffin M. (Author), Liao, Katherine P. (Author), Palmer, Nathan P. (Author), Churchill, Susanne (Author), Shadick, Nancy A. (Author), Szolovits, Peter (Author), Murphy, Shawn N. (Author), Kohane, Isaac S. (Author), Cai, Tianxi (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Springer Nature, 2019-11-11T16:22:01Z.
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